Understanding how the world's flora and fauna will respond to bioenergy expansion is critical. This issue is particularly pronounced considering bioenergy's potential role as a driver of land‐use change, the variety of production crops being considered and currently used for biomass, and the diversity of ecosystems that can potentially supply land for bioenergy across the planet. We conducted 2 global meta‐analyses to determine how 8 of the most commonly used bioenergy crops may affect site‐level biodiversity. One search was directed at finding data on biodiversity in different production land uses and the other at extracting energy‐yield estimates of potential bioenergy crops. We used linear mixed‐effect models to test whether effects on biodiversity varied with different individual bioenergy crop species, estimated energy yield, first‐ or second‐generation crops, type of reference ecosystem considered, and magnitude of vertical change in habitat structure between any given crop and the reference ecosystem. Species diversity and abundance were generally lower in crops considered for bioenergy relative to the natural ecosystems they may replace. First‐generation crops, derived from oils, sugars, and starches, tended to have greater effects than second‐generation crops, derived from lignocellulose, woody crops, or residues. Crop yield had nonlinear effects on abundance and, to a lesser extent, overall biodiversity; biodiversity effects were driven by negative yield effects for birds but not other taxa. Our results emphasize that replacing natural ecosystems with bioenergy crops across the planet will largely be detrimental for biodiversity, with first generation and high‐yield crops having the strongest negative effects. We argue that meeting energy goals with bioenergy using existing marginal lands or biomass extraction within existing production landscapes may provide more biodiversity‐friendly alternatives than conversion of natural ecosystems for biofuel production.

The Amazon River basin contains a vast diversity of lotic habitats and accompanying hydrological regimes. Further understanding the spatial distribution of flow regimes across the Amazon can be useful for recognizing riverine ecohydrological processes and informing river management and conservation, especially in areas with limited or inconsistent streamflow monitoring. This study compares four inductive approaches for classifying streamflow regimes across the Amazon using an unprecedented compilation of streamflow records from Bolivia, Brazil, Colombia, Ecuador, and Peru. Inductive classification schemes use attributes of streamflow data to categorize river reaches into similar classes, which then may be generalized to understand streamflow behaviour at the basin scale. In this study, classification was accomplished through hierarchical clustering of 67 flow metrics calculated using indicators of hydrologic alteration (IHA) and daily streamflow data from median annual hydrographs (MAHs) for 404 stations (representing >7,000 station‐years) across five Amazonian countries. Classification was performed using both flow magnitude‐inclusive and flow magnitude‐independent datasets. For flow magnitude‐independent methods, optimal solutions included six or seven primary hydrological classes for IHA and MAH datasets; for approaches that retained magnitude, variance was sufficiently large to prevent convergence to a specific number of classes. Across methods, class membership was strongly associated with the timing, frequency, and rate of change of flow, and spatially coherent clusters were associated with seasonal, elevational, and stream‐order gradients. These results highlight the diversity of flow regimes across the Amazon and provide a framework for studying relationships between hydrological regimes and ecological responses in the context of changing climate, land use, and human‐induced hydrological alteration. The methodology applied provides a data‐driven approach for classifying flow regimes based on observed data. When coupled with ecological knowledge and expertise, these classifications can be used to develop ecohydrologically informed and management‐relevant conservation practices.

Riverine floodplains are biologically diverse and productive ecosystems. Although tropical floodplains remain relatively conserved and ecologically functional compared to those at higher latitudes, they face accelerated hydropower development, climate change, and deforestation. Alterations to the flood pulse could act synergistically with other drivers of change to promote profound ecological state change at a large spatial scale. State change occurs when an ecosystem reaches a critical threshold or tipping point, which leads to an alternative qualitative state for the ecosystem. Visualizing an alternative state for Amazonian floodplains is not straightforward. Yet, it is critical to recognize that changes to the flood pulse could push tropical floodplain ecosystems over a tipping point with cascading adverse effects on biodiversity and ecosystem services. We characterize the Amazonian flood pulse regime, summarize evidence of flood pulse change, assess potential ecological repercussions, and provide a monitoring framework for tracking flood pulse change and detecting biotic responses.

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